Modern computer systems offer a variety of services to remote operators. For some of these services, it may be necessary or advisable that the computer systems be able to ensure that the remote operator requesting such services is, in fact, a human being and not itself an automation, that is to say an automated (e.g., a computer) system. For example, websites that offer free e-mail accounts, or web services that offer items for sale may want to ensure that the remote user accessing their services is indeed a human acting in a deliberate manner and not a machine. For another example, a banking website may wish to confirm that transfer requests from its website were initiated by customers and not automated fraud processes, which could flood the site with unsolicited requests.
One of the techniques by which modern computer systems can achieve such a goal of determining whether a user attempting to access the system is a human or a machine is with use of what is known as a “human interactive proof” (“HIP”) or a “reverse Turing test.” See e.g., U.S. Patent Application Publication Number 2007/0026372 (“Huelsbergen”) at page 1, paragraph [3]. According to Huelsbergen, HIP presents a user with a puzzle that is hard or expensive in time for a machine to solve, whereas a reverse Turing test is a challenge posed by a computer which only a human should be able to solve. Id. Reverse Turing tests have become known as CAPTCHAs (completely automated public Turing test to tell computers and human apart). Huelsbergen at page 1, paragraph [5].
Most typically, these systems work by displaying distorted text images (e.g., an English language word containing a sequence of alphabetic characters) to the remote operator and asking the remote operator to identify the distorted image. Apparently, the distorted text images are sufficiently complex as make computer recognition difficult, yet, these images are sufficiently obvious to make human recognition trivial.
As noted in Huelsbergen prior art CAPTCHAs and HIPs often have the limitation that the challenge posed is either too easy to break (e.g., solve) by, for example, a machine guessing the correct answer a significant percentage of time, or too difficult for humans. In particular, with respect to CAPTCHAs, modern optical character recognition (“OCR”) systems are becoming sophisticated enough to solve most image puzzles. Furthermore, the distorted text images used in CAPTCHAs fail to comply with America's With Disability Act (“ADA”). Users who are blind or partially sighted are unable to resolve the images, and, as such, unable to fully use the services. To remedy this problem, CAPTCHAs use Alt-tags, which may be heard by screen reading software. These tags carry the text information displayed in the image. This makes the text information readily available to automated processes—defeating CAPTCHA itself. Therefore, there is a need for a method that overcomes the above-stated problems and at the same time effectively distinguishes between human and automated responses for machine access with use of a human interactive proof or reverse Turing test. In particular, there is a need for a method that allows use of Alt-tags or text without compromising the effectiveness of the test.